I am Rohit Dandamudi, more commonly known as diru1100 in IRC and all other sites. I am currently doing my final year in Computer Science and Engineering at Chaitanya Bharathi Institute of Technology, Hyderabad. This summer, I had the wonderful opportunity to work with MetaBrainz Foundation and it’s my first time participating in GSoC. I worked on the SpamBrainz project under the guidance of yvanzo to make a step forward on eliminating spam in MusicBrainz.
How it started
I started looking for some cool projects to apply for GSoC, eventually, after going through some which were involved in the web development side, I finally got to know about the MetaBrainz Foundation, and it was already pretty late (around 2½ weeks before the proposal deadline), most of my fellow GSoCers were already in good rapport with the community by then. After looking through the project ideas, I wanted to do my project on CritiqueBrainz, but later I found out that it’s not considered for this year. In the end, I liked the concept of SpamBrainz and how it involves a good combination (Deep Learning and Web Development) of technologies. After browsing through the project I understood what I could and tried to make some changes to the codebase and was successfully able to run the model and add some documentation. Finally, I submitted the proposal, which got accepted.
My proposal was focused on extending the work done by Leo as part of GSoC 2018. It mainly involved the following:
- Do the research and implement online learning to:
- Update the model dynamically as new variations of editor spam accounts appear.
- Make the model self-sufficient without depending on a particular file or a batch of data.
- Explore different types of learnings that are applicable to enhance LodBrok and for better performance in production.
- Complete SpamBrainz API to:
- Use and update the model with API calls.
- Connect LodBrok with MusicBrainz Server.
- Do detailed documentation to make the project more public and involve more contributors
LodBrok model improvements
- Challenges faced initially were:
- There was less documentation.
- No access to real data.
- This made it a little difficult to understand the model, how it works, and what parameters are present, what is considered as spam or not, etc.
- To overcome this obstacle in the future, I have written a dummy data generation script.
- Have predicted using the model trained with generated data and got 100% accuracy against test data.
- Retrained the model to simulate online learning after doing a lot of research and considering the use case of LodBrok in MusicBrainz.
- Retrained the model with the simulation of taking spam as a non_spam account and it was able to predict the new learnings while still being able to remember the original non_spam accounts.
- Added detailed documentation about the model covering its usage, internal working, how to replicate locally with the help of images, helper functions.
- Finally, with all these included, the spambrainz_ml repo has released it’s first release v-0.1 with necessary binaries.
- Here is a navigation diagram explaining which notebooks and datasets are connected and the relationship between them.
Research for model live update
- To implement the online learning part I had to explore and test different methods with the generated dataset and LodBrok model. For this, I had to explore various resources such as Keras’ community forums, research papers, StackOverflow, courses, and blogs.
- A few of the interesting findings I have tested out were:
- Retraining the model
- This seemed to be the most obvious and easy fix to upgrade the model.
- This StackOverflow answer explains how retraining is done
- But some things to consider here are: a separate db has to be maintained to store the dataset and should be constantly updated by SpamNinja.
- This is not feasible overtime
- A lot of work is done to just transfer the data.
- Transfer Learning (TL)
- Official Keras blog explanation about the feature extraction and fine-tuning methods of TL.
- Transfer Learning mainly involves deriving a new model from a pre-existing successful model (LodBrok) known as feature extraction to tackle similar cases.
- I was inspired by the fine-tuning feature of Transfer learning which has a similar learning method as the one I implemented.
- Online Transfer Learning (OTL)
- This, as the name suggests is a combination of online learning and Transfer learning, which helps us to define models that can learn to classify similar spam accounts in MetabBrainz.
- This research explains about OTL and it’s use cases in the production environment.
- The concept of model drift:
- This article explains how the model degrades over time, the reasons for this, and how to handle it without depending on the production environment.
- This is useful to know as it is needed when the model is finally in production handling real data.
- Retraining the model
- In the end, I decided to go with refitting the model with a slow static learning rate, this seemed to be the best solution for the following reasons:
- No need to store editor details for false-positive and false-negative cases respecting MetaBrainz’s data privacy rules.
- The model won’t go through catastrophic forgetting (forget old learnings of what is spam or not) and will be able to learn new patterns in spam accounts over time.
- The structure of the data isn’t changing over time (editor account fields remain the same).
- Resources which helped me make this decision:
- Keras community help discussion about the same exact problem (online learning in Keras for an LSTM model [LodBrok])
- StackOverflow answer explaining catastrophic forgetting and role of fit function
- Machine Learning mastery blog article explaining the importance of learning rate on a model
- Reading research papers similar to this one covering online deep learning and consulting professors.
- Incorporated the above research in SpamBrainz API, which consists of 2 endpoints, namely:
/predictto return classification results by LodBrok for the editor accounts
/trainto retrain the model with incorrect results sent to SpamNinja respectively
- After discussing with Leo, I decided to implement the API using Flask and Redis combination. Going with Redis over RabbitMQ for this API is feasible as the API is pretty lightweight and has at most 2 events.
- Documented the entire API, with internal working, steps to replicate, and images to understand the results obtained.
- Completed dockerization of SpamBrainz_API for easier integration and testing with MusicBrainz docker.
- This diagram explains the current workflow of the implemented API:
Challenges ahead and future of SpamBrainz
- The API has to be integrated with MusicBrainz and should undergo more testing with real live data, currently, my focus is on this part.
- To extend online learning to other use cases in MetaBrainz through Transfer Learning and Online Transfer Learning.
- Also looking forward to writing a research paper about the work done, and eventually publish it in IEEE transactions, as I plan on using SpamBrainz as my final year major project.
Special thanks to…
- My mentor, YvanZo for being incredibly patient with me, helping me create quality commits, and overall making me a better programmer. Have always learned something new in every interaction with him.
- LeoVerto, for helping me out whenever stuck and getting me up to date with the project.
- MetaBrainz Foundation, for creating an open, inclusive, and productive environment to build some amazing stuff.
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